{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7f996186",
   "metadata": {},
   "source": [
    "# Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a325451a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "908b64ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "my_idx\n",
       "1            100\n",
       "2              a\n",
       "3    {'dic1': 5}\n",
       "Name: my_name, dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(data = [100, 'a', {'dic1':5}],\n",
    "              index = pd.Index([1,2,3], name='my_idx'),\n",
    "              dtype = 'object',\n",
    "              name = 'my_name')\n",
    "s\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ee43795f",
   "metadata": {},
   "outputs": [],
   "source": [
    "s1 = pd.Series(\n",
    "    data = [67,78,79],\n",
    "    index=pd.Index(['语文','数学','英语'],name='学科')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b1d52cc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "学科\n",
       "语文    67\n",
       "数学    78\n",
       "英语    79\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9f735857",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([67, 78, 79], dtype=int64)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c9e4b1f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['语文', '数学', '英语'], dtype='object', name='学科')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c4f8c97b",
   "metadata": {},
   "outputs": [],
   "source": [
    "s2 =pd.Series(\n",
    "    data =[66,67,68]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "11fe20e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    66\n",
       "1    67\n",
       "2    68\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8a8f9aa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1['语文']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9394bff5",
   "metadata": {},
   "source": [
    "# DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "19663b98",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [[1, 'a', 1.2], [2, 'b', 2.2], [3, 'c', 3.2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9a68aeb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data = data,\n",
    "                  index = ['row_0','row_1','row_2'],\n",
    "                  columns=['col_0', 'col_1', 'col_2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e170d16d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col_0</th>\n",
       "      <th>col_1</th>\n",
       "      <th>col_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>row_0</th>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row_1</th>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row_2</th>\n",
       "      <td>3</td>\n",
       "      <td>c</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       col_0 col_1  col_2\n",
       "row_0      1     a    1.2\n",
       "row_1      2     b    2.2\n",
       "row_2      3     c    3.2"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "663a672a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data={\n",
    "    'col_0':[1,2,3],\n",
    "    'col_1':['a','b','c'],\n",
    "    'col_2':['1.2','2.2','3.2']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dd5deac3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df =pd.DataFrame(\n",
    "    data=data,\n",
    "    index=['row_0','row_1','row_2']\n",
    "\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2e3f6a27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col_0</th>\n",
       "      <th>col_1</th>\n",
       "      <th>col_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>row_0</th>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row_1</th>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row_2</th>\n",
       "      <td>3</td>\n",
       "      <td>c</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       col_0 col_1 col_2\n",
       "row_0      1     a   1.2\n",
       "row_1      2     b   2.2\n",
       "row_2      3     c   3.2"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "009531eb",
   "metadata": {},
   "source": [
    "## DataFrame取值的一般方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a8bbbbc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "row_0    1\n",
       "row_1    2\n",
       "row_2    3\n",
       "Name: col_0, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['col_0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "016eb04b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col_0</th>\n",
       "      <th>col_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>row_0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row_1</th>\n",
       "      <td>2</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row_2</th>\n",
       "      <td>3</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       col_0 col_2\n",
       "row_0      1   1.2\n",
       "row_1      2   2.2\n",
       "row_2      3   3.2"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['col_0','col_2']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "358dba73",
   "metadata": {},
   "source": [
    "* iloc :强大的切片取值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ac12748f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col_1</th>\n",
       "      <th>col_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>row_1</th>\n",
       "      <td>b</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row_2</th>\n",
       "      <td>c</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      col_1 col_2\n",
       "row_1     b   2.2\n",
       "row_2     c   3.2"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1:3,1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2f6bb40",
   "metadata": {},
   "source": [
    "* 课后练习(参考pandas cheat sheet)：\n",
    "> 1. iloc\n",
    "> 2. loc\n",
    "> 3. iat\n",
    "> 4. at"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15a5e393",
   "metadata": {},
   "source": [
    "# 常用的基本函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6fdede01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Transfer</th>\n",
       "      <th>Test_Number</th>\n",
       "      <th>Test_Date</th>\n",
       "      <th>Time_Record</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Gaopeng Yang</td>\n",
       "      <td>Female</td>\n",
       "      <td>158.9</td>\n",
       "      <td>46.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/10/5</td>\n",
       "      <td>0:04:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Changqiang You</td>\n",
       "      <td>Male</td>\n",
       "      <td>166.5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/9/4</td>\n",
       "      <td>0:04:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Mei Sun</td>\n",
       "      <td>Male</td>\n",
       "      <td>188.9</td>\n",
       "      <td>89.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/9/12</td>\n",
       "      <td>0:05:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Xiaojuan Sun</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2020/1/3</td>\n",
       "      <td>0:04:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Gaojuan You</td>\n",
       "      <td>Male</td>\n",
       "      <td>174.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/11/6</td>\n",
       "      <td>0:05:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Xiaojuan Sun</td>\n",
       "      <td>Female</td>\n",
       "      <td>153.9</td>\n",
       "      <td>46.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/10/17</td>\n",
       "      <td>0:04:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Li Zhao</td>\n",
       "      <td>Female</td>\n",
       "      <td>160.9</td>\n",
       "      <td>50.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2019/9/22</td>\n",
       "      <td>0:04:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Chengqiang Chu</td>\n",
       "      <td>Female</td>\n",
       "      <td>153.9</td>\n",
       "      <td>45.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2020/1/5</td>\n",
       "      <td>0:04:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Chengmei Shen</td>\n",
       "      <td>Male</td>\n",
       "      <td>175.3</td>\n",
       "      <td>71.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2020/1/7</td>\n",
       "      <td>0:04:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Chunpeng Lv</td>\n",
       "      <td>Male</td>\n",
       "      <td>155.7</td>\n",
       "      <td>51.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/11/6</td>\n",
       "      <td>0:05:05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            School      Grade            Name  Gender  Height  \\\n",
       "0    Shanghai Jiao Tong University   Freshman    Gaopeng Yang  Female   158.9   \n",
       "1                Peking University   Freshman  Changqiang You    Male   166.5   \n",
       "2    Shanghai Jiao Tong University     Senior         Mei Sun    Male   188.9   \n",
       "3                 Fudan University  Sophomore    Xiaojuan Sun  Female     NaN   \n",
       "4                 Fudan University  Sophomore     Gaojuan You    Male   174.0   \n",
       "..                             ...        ...             ...     ...     ...   \n",
       "195               Fudan University     Junior    Xiaojuan Sun  Female   153.9   \n",
       "196            Tsinghua University     Senior         Li Zhao  Female   160.9   \n",
       "197  Shanghai Jiao Tong University     Senior  Chengqiang Chu  Female   153.9   \n",
       "198  Shanghai Jiao Tong University     Senior   Chengmei Shen    Male   175.3   \n",
       "199            Tsinghua University  Sophomore     Chunpeng Lv    Male   155.7   \n",
       "\n",
       "     Weight Transfer  Test_Number   Test_Date Time_Record  \n",
       "0      46.0        N            1   2019/10/5     0:04:34  \n",
       "1      70.0        N            1    2019/9/4     0:04:20  \n",
       "2      89.0        N            2   2019/9/12     0:05:22  \n",
       "3      41.0        N            2    2020/1/3     0:04:08  \n",
       "4      74.0        N            2   2019/11/6     0:05:22  \n",
       "..      ...      ...          ...         ...         ...  \n",
       "195    46.0        N            2  2019/10/17     0:04:31  \n",
       "196    50.0        N            3   2019/9/22     0:04:03  \n",
       "197    45.0        N            1    2020/1/5     0:04:48  \n",
       "198    71.0        N            2    2020/1/7     0:04:58  \n",
       "199    51.0        N            1   2019/11/6     0:05:05  \n",
       "\n",
       "[200 rows x 10 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df =pd.read_csv(\"D:\\新建文件夹 (3)\\data_analysis-master\\data_analysis-master\\week02\\data\\learn_pandas.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "62cd6244",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['School', 'Grade', 'Name', 'Gender', 'Height', 'Weight', 'Transfer',\n",
       "       'Test_Number', 'Test_Date', 'Time_Record'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "951dd21f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gaopeng Yang</td>\n",
       "      <td>158.9</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Changqiang You</td>\n",
       "      <td>166.5</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mei Sun</td>\n",
       "      <td>188.9</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Xiaojuan Sun</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gaojuan You</td>\n",
       "      <td>174.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>Xiaojuan Sun</td>\n",
       "      <td>153.9</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>Li Zhao</td>\n",
       "      <td>160.9</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>Chengqiang Chu</td>\n",
       "      <td>153.9</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>Chengmei Shen</td>\n",
       "      <td>175.3</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>Chunpeng Lv</td>\n",
       "      <td>155.7</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name  Height  Weight\n",
       "0      Gaopeng Yang   158.9    46.0\n",
       "1    Changqiang You   166.5    70.0\n",
       "2           Mei Sun   188.9    89.0\n",
       "3      Xiaojuan Sun     NaN    41.0\n",
       "4       Gaojuan You   174.0    74.0\n",
       "..              ...     ...     ...\n",
       "195    Xiaojuan Sun   153.9    46.0\n",
       "196         Li Zhao   160.9    50.0\n",
       "197  Chengqiang Chu   153.9    45.0\n",
       "198   Chengmei Shen   175.3    71.0\n",
       "199     Chunpeng Lv   155.7    51.0\n",
       "\n",
       "[200 rows x 3 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Name','Height','Weight']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "42be923d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>School</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Transfer</th>\n",
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       "      <th>Time_Record</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Gaopeng Yang</td>\n",
       "      <td>Female</td>\n",
       "      <td>158.9</td>\n",
       "      <td>46.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/10/5</td>\n",
       "      <td>0:04:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Changqiang You</td>\n",
       "      <td>Male</td>\n",
       "      <td>166.5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/9/4</td>\n",
       "      <td>0:04:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Mei Sun</td>\n",
       "      <td>Male</td>\n",
       "      <td>188.9</td>\n",
       "      <td>89.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/9/12</td>\n",
       "      <td>0:05:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Xiaojuan Sun</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2020/1/3</td>\n",
       "      <td>0:04:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Gaojuan You</td>\n",
       "      <td>Male</td>\n",
       "      <td>174.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/11/6</td>\n",
       "      <td>0:05:22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          School      Grade            Name  Gender  Height  \\\n",
       "0  Shanghai Jiao Tong University   Freshman    Gaopeng Yang  Female   158.9   \n",
       "1              Peking University   Freshman  Changqiang You    Male   166.5   \n",
       "2  Shanghai Jiao Tong University     Senior         Mei Sun    Male   188.9   \n",
       "3               Fudan University  Sophomore    Xiaojuan Sun  Female     NaN   \n",
       "4               Fudan University  Sophomore     Gaojuan You    Male   174.0   \n",
       "\n",
       "   Weight Transfer  Test_Number  Test_Date Time_Record  \n",
       "0    46.0        N            1  2019/10/5     0:04:34  \n",
       "1    70.0        N            1   2019/9/4     0:04:20  \n",
       "2    89.0        N            2  2019/9/12     0:05:22  \n",
       "3    41.0        N            2   2020/1/3     0:04:08  \n",
       "4    74.0        N            2  2019/11/6     0:05:22  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1b8fc46f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Transfer</th>\n",
       "      <th>Test_Number</th>\n",
       "      <th>Test_Date</th>\n",
       "      <th>Time_Record</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Xiaojuan Sun</td>\n",
       "      <td>Female</td>\n",
       "      <td>153.9</td>\n",
       "      <td>46.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/10/17</td>\n",
       "      <td>0:04:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Li Zhao</td>\n",
       "      <td>Female</td>\n",
       "      <td>160.9</td>\n",
       "      <td>50.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2019/9/22</td>\n",
       "      <td>0:04:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Chengqiang Chu</td>\n",
       "      <td>Female</td>\n",
       "      <td>153.9</td>\n",
       "      <td>45.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2020/1/5</td>\n",
       "      <td>0:04:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Chengmei Shen</td>\n",
       "      <td>Male</td>\n",
       "      <td>175.3</td>\n",
       "      <td>71.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2020/1/7</td>\n",
       "      <td>0:04:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Chunpeng Lv</td>\n",
       "      <td>Male</td>\n",
       "      <td>155.7</td>\n",
       "      <td>51.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/11/6</td>\n",
       "      <td>0:05:05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            School      Grade            Name  Gender  Height  \\\n",
       "195               Fudan University     Junior    Xiaojuan Sun  Female   153.9   \n",
       "196            Tsinghua University     Senior         Li Zhao  Female   160.9   \n",
       "197  Shanghai Jiao Tong University     Senior  Chengqiang Chu  Female   153.9   \n",
       "198  Shanghai Jiao Tong University     Senior   Chengmei Shen    Male   175.3   \n",
       "199            Tsinghua University  Sophomore     Chunpeng Lv    Male   155.7   \n",
       "\n",
       "     Weight Transfer  Test_Number   Test_Date Time_Record  \n",
       "195    46.0        N            2  2019/10/17     0:04:31  \n",
       "196    50.0        N            3   2019/9/22     0:04:03  \n",
       "197    45.0        N            1    2020/1/5     0:04:48  \n",
       "198    71.0        N            2    2020/1/7     0:04:58  \n",
       "199    51.0        N            1   2019/11/6     0:05:05  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7828027f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 10 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   School       200 non-null    object \n",
      " 1   Grade        200 non-null    object \n",
      " 2   Name         200 non-null    object \n",
      " 3   Gender       200 non-null    object \n",
      " 4   Height       183 non-null    float64\n",
      " 5   Weight       189 non-null    float64\n",
      " 6   Transfer     188 non-null    object \n",
      " 7   Test_Number  200 non-null    int64  \n",
      " 8   Test_Date    200 non-null    object \n",
      " 9   Time_Record  200 non-null    object \n",
      "dtypes: float64(2), int64(1), object(7)\n",
      "memory usage: 15.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7ef662ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Test_Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>183.000000</td>\n",
       "      <td>189.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>163.218033</td>\n",
       "      <td>55.015873</td>\n",
       "      <td>1.645000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.608879</td>\n",
       "      <td>12.824294</td>\n",
       "      <td>0.722207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>145.400000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>157.150000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>161.900000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>167.500000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>193.900000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Height      Weight  Test_Number\n",
       "count  183.000000  189.000000   200.000000\n",
       "mean   163.218033   55.015873     1.645000\n",
       "std      8.608879   12.824294     0.722207\n",
       "min    145.400000   34.000000     1.000000\n",
       "25%    157.150000   46.000000     1.000000\n",
       "50%    161.900000   51.000000     1.500000\n",
       "75%    167.500000   65.000000     2.000000\n",
       "max    193.900000   89.000000     3.000000"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97de4c0f",
   "metadata": {},
   "source": [
    "## 特征统计函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "61610459",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>158.9</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>166.5</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>188.9</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>174.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>153.9</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>160.9</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>153.9</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>175.3</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>155.7</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Height  Weight\n",
       "0     158.9    46.0\n",
       "1     166.5    70.0\n",
       "2     188.9    89.0\n",
       "3       NaN    41.0\n",
       "4     174.0    74.0\n",
       "..      ...     ...\n",
       "195   153.9    46.0\n",
       "196   160.9    50.0\n",
       "197   153.9    45.0\n",
       "198   175.3    71.0\n",
       "199   155.7    51.0\n",
       "\n",
       "[200 rows x 2 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_demo=df[['Height','Weight']]\n",
    "df_demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7cb865d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Height    163.218033\n",
       "Weight     55.015873\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_demo.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "37beb5f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Height    183\n",
       "Weight    189\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_demo.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7654eb87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Height    193\n",
       "Weight      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_demo.idxmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e338be8e",
   "metadata": {},
   "source": [
    "## 唯一值函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0945b713",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tsinghua University              69\n",
       "Shanghai Jiao Tong University    57\n",
       "Fudan University                 40\n",
       "Peking University                34\n",
       "Name: School, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['School'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7b8ec9d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Shanghai Jiao Tong University', 'Peking University',\n",
       "       'Fudan University', 'Tsinghua University'], dtype=object)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['School'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf80413d",
   "metadata": {},
   "source": [
    "* query()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b1908a43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Transfer</th>\n",
       "      <th>Test_Number</th>\n",
       "      <th>Test_Date</th>\n",
       "      <th>Time_Record</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Changqiang You</td>\n",
       "      <td>Male</td>\n",
       "      <td>166.5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/9/4</td>\n",
       "      <td>0:04:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Juan Xu</td>\n",
       "      <td>Female</td>\n",
       "      <td>164.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2019/10/5</td>\n",
       "      <td>0:04:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Changjuan You</td>\n",
       "      <td>Female</td>\n",
       "      <td>161.4</td>\n",
       "      <td>47.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/10/5</td>\n",
       "      <td>0:04:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Changmei Xu</td>\n",
       "      <td>Female</td>\n",
       "      <td>151.6</td>\n",
       "      <td>43.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2020/1/3</td>\n",
       "      <td>0:04:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Changli Lv</td>\n",
       "      <td>Female</td>\n",
       "      <td>148.7</td>\n",
       "      <td>41.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/11/13</td>\n",
       "      <td>0:04:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Gaopeng Shi</td>\n",
       "      <td>Female</td>\n",
       "      <td>162.9</td>\n",
       "      <td>48.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/9/12</td>\n",
       "      <td>0:04:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Gaoli Zhao</td>\n",
       "      <td>Male</td>\n",
       "      <td>175.4</td>\n",
       "      <td>78.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/10/8</td>\n",
       "      <td>0:03:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Xiaojuan Qin</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/12/10</td>\n",
       "      <td>0:04:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Qiang Han</td>\n",
       "      <td>Male</td>\n",
       "      <td>185.3</td>\n",
       "      <td>87.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2020/1/7</td>\n",
       "      <td>0:03:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Quan Chu</td>\n",
       "      <td>Female</td>\n",
       "      <td>154.7</td>\n",
       "      <td>43.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/11/28</td>\n",
       "      <td>0:04:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Xiaojuan Chu</td>\n",
       "      <td>Male</td>\n",
       "      <td>162.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>3</td>\n",
       "      <td>2019/11/29</td>\n",
       "      <td>0:03:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Changquan Chu</td>\n",
       "      <td>Female</td>\n",
       "      <td>159.6</td>\n",
       "      <td>45.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/12/9</td>\n",
       "      <td>0:04:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Gaoli Xu</td>\n",
       "      <td>Female</td>\n",
       "      <td>157.3</td>\n",
       "      <td>48.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/12/11</td>\n",
       "      <td>0:05:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Xiaopeng Qin</td>\n",
       "      <td>Male</td>\n",
       "      <td>172.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/12/23</td>\n",
       "      <td>0:05:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Gaoquan Zhou</td>\n",
       "      <td>Male</td>\n",
       "      <td>166.8</td>\n",
       "      <td>70.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/9/5</td>\n",
       "      <td>0:04:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Qiang You</td>\n",
       "      <td>Female</td>\n",
       "      <td>170.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2019/12/31</td>\n",
       "      <td>0:04:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Mei Xu</td>\n",
       "      <td>Female</td>\n",
       "      <td>154.2</td>\n",
       "      <td>39.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/11/5</td>\n",
       "      <td>0:04:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Feng Zheng</td>\n",
       "      <td>Female</td>\n",
       "      <td>162.6</td>\n",
       "      <td>49.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/11/5</td>\n",
       "      <td>0:04:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Xiaopeng Han</td>\n",
       "      <td>Female</td>\n",
       "      <td>164.1</td>\n",
       "      <td>53.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/12/18</td>\n",
       "      <td>0:05:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Changmei Feng</td>\n",
       "      <td>Female</td>\n",
       "      <td>163.8</td>\n",
       "      <td>56.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2019/11/8</td>\n",
       "      <td>0:04:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Changpeng Zhao</td>\n",
       "      <td>Male</td>\n",
       "      <td>181.3</td>\n",
       "      <td>83.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/10/24</td>\n",
       "      <td>0:04:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Xiaoli Zhou</td>\n",
       "      <td>Female</td>\n",
       "      <td>166.8</td>\n",
       "      <td>55.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/10/28</td>\n",
       "      <td>0:05:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Chengli Zhao</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/10/13</td>\n",
       "      <td>0:03:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Feng Zhao</td>\n",
       "      <td>Male</td>\n",
       "      <td>167.2</td>\n",
       "      <td>66.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2020/1/3</td>\n",
       "      <td>0:04:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Peng Han</td>\n",
       "      <td>Female</td>\n",
       "      <td>147.8</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/9/19</td>\n",
       "      <td>0:03:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Changquan Han</td>\n",
       "      <td>Male</td>\n",
       "      <td>173.4</td>\n",
       "      <td>77.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/11/4</td>\n",
       "      <td>0:03:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Mei Feng</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2019/9/28</td>\n",
       "      <td>0:05:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Chunpeng Qian</td>\n",
       "      <td>Female</td>\n",
       "      <td>161.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/11/10</td>\n",
       "      <td>0:04:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Qiang Zhang</td>\n",
       "      <td>Female</td>\n",
       "      <td>152.7</td>\n",
       "      <td>43.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/11/30</td>\n",
       "      <td>0:05:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Juan You</td>\n",
       "      <td>Male</td>\n",
       "      <td>169.2</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/10/31</td>\n",
       "      <td>0:05:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Chengpeng Zhao</td>\n",
       "      <td>Female</td>\n",
       "      <td>156.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/9/2</td>\n",
       "      <td>0:03:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Xiaofeng Zhao</td>\n",
       "      <td>Female</td>\n",
       "      <td>159.9</td>\n",
       "      <td>46.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/10/17</td>\n",
       "      <td>0:05:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Chunmei Wang</td>\n",
       "      <td>Female</td>\n",
       "      <td>151.2</td>\n",
       "      <td>43.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/12/10</td>\n",
       "      <td>0:04:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Yanmei Qian</td>\n",
       "      <td>Female</td>\n",
       "      <td>160.3</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/12/3</td>\n",
       "      <td>0:05:08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                School      Grade            Name  Gender  Height  Weight  \\\n",
       "1    Peking University   Freshman  Changqiang You    Male   166.5    70.0   \n",
       "9    Peking University     Junior         Juan Xu  Female   164.8     NaN   \n",
       "20   Peking University     Junior   Changjuan You  Female   161.4    47.0   \n",
       "29   Peking University  Sophomore     Changmei Xu  Female   151.6    43.0   \n",
       "30   Peking University     Senior      Changli Lv  Female   148.7    41.0   \n",
       "32   Peking University   Freshman     Gaopeng Shi  Female   162.9    48.0   \n",
       "35   Peking University   Freshman      Gaoli Zhao    Male   175.4    78.0   \n",
       "36   Peking University   Freshman    Xiaojuan Qin    Male     NaN    79.0   \n",
       "38   Peking University   Freshman       Qiang Han    Male   185.3    87.0   \n",
       "45   Peking University   Freshman        Quan Chu  Female   154.7    43.0   \n",
       "54   Peking University   Freshman    Xiaojuan Chu    Male   162.4    58.0   \n",
       "57   Peking University   Freshman   Changquan Chu  Female   159.6    45.0   \n",
       "59   Peking University     Junior        Gaoli Xu  Female   157.3    48.0   \n",
       "61   Peking University  Sophomore    Xiaopeng Qin    Male   172.8     NaN   \n",
       "72   Peking University     Junior    Gaoquan Zhou    Male   166.8    70.0   \n",
       "75   Peking University     Junior       Qiang You  Female   170.0    56.0   \n",
       "83   Peking University  Sophomore          Mei Xu  Female   154.2    39.0   \n",
       "86   Peking University     Senior      Feng Zheng  Female   162.6    49.0   \n",
       "88   Peking University   Freshman    Xiaopeng Han  Female   164.1    53.0   \n",
       "96   Peking University   Freshman   Changmei Feng  Female   163.8    56.0   \n",
       "99   Peking University   Freshman  Changpeng Zhao    Male   181.3    83.0   \n",
       "101  Peking University  Sophomore     Xiaoli Zhou  Female   166.8    55.0   \n",
       "102  Peking University     Junior    Chengli Zhao    Male     NaN     NaN   \n",
       "116  Peking University     Senior       Feng Zhao    Male   167.2    66.0   \n",
       "120  Peking University  Sophomore        Peng Han  Female   147.8    34.0   \n",
       "127  Peking University     Senior   Changquan Han    Male   173.4    77.0   \n",
       "130  Peking University     Senior        Mei Feng  Female     NaN    51.0   \n",
       "132  Peking University     Senior   Chunpeng Qian  Female   161.6     NaN   \n",
       "140  Peking University   Freshman     Qiang Zhang  Female   152.7    43.0   \n",
       "147  Peking University     Senior        Juan You    Male   169.2    69.0   \n",
       "159  Peking University     Junior  Chengpeng Zhao  Female   156.0    44.0   \n",
       "183  Peking University     Junior   Xiaofeng Zhao  Female   159.9    46.0   \n",
       "185  Peking University   Freshman    Chunmei Wang  Female   151.2    43.0   \n",
       "194  Peking University     Senior     Yanmei Qian  Female   160.3    49.0   \n",
       "\n",
       "    Transfer  Test_Number   Test_Date Time_Record  \n",
       "1          N            1    2019/9/4     0:04:20  \n",
       "9          N            3   2019/10/5     0:04:05  \n",
       "20         N            1   2019/10/5     0:04:08  \n",
       "29         N            2    2020/1/3     0:04:28  \n",
       "30         N            2  2019/11/13     0:04:54  \n",
       "32         N            1   2019/9/12     0:04:58  \n",
       "35         N            2   2019/10/8     0:03:32  \n",
       "36         Y            1  2019/12/10     0:04:10  \n",
       "38         N            3    2020/1/7     0:03:58  \n",
       "45         N            1  2019/11/28     0:04:47  \n",
       "54         Y            3  2019/11/29     0:03:42  \n",
       "57         N            2   2019/12/9     0:04:18  \n",
       "59         N            2  2019/12/11     0:05:13  \n",
       "61         N            1  2019/12/23     0:05:29  \n",
       "72         N            2    2019/9/5     0:04:24  \n",
       "75         N            3  2019/12/31     0:04:27  \n",
       "83         N            2   2019/11/5     0:04:29  \n",
       "86         N            2   2019/11/5     0:04:11  \n",
       "88         N            1  2019/12/18     0:05:20  \n",
       "96         N            3   2019/11/8     0:04:41  \n",
       "99         N            2  2019/10/24     0:04:08  \n",
       "101        N            1  2019/10/28     0:05:24  \n",
       "102      NaN            1  2019/10/13     0:03:55  \n",
       "116        N            1    2020/1/3     0:04:56  \n",
       "120      NaN            2   2019/9/19     0:03:32  \n",
       "127        N            1   2019/11/4     0:03:56  \n",
       "130        N            3   2019/9/28     0:05:29  \n",
       "132        N            1  2019/11/10     0:04:10  \n",
       "140        N            1  2019/11/30     0:05:27  \n",
       "147      NaN            1  2019/10/31     0:05:28  \n",
       "159        N            1    2019/9/2     0:03:53  \n",
       "183        N            1  2019/10/17     0:05:20  \n",
       "185        N            2  2019/12/10     0:04:24  \n",
       "194      NaN            1   2019/12/3     0:05:08  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query(\" School == 'Peking University'\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28ba82e0",
   "metadata": {},
   "source": [
    "## 实践一\n",
    "* 请计算所有不同学校的身高，体重的均值，最大值，最小值\n",
    "* 请计算所有不同学校的男女比例情况\n",
    "* 统计：不同学校的Grade的数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a5da8043",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\86135\\AppData\\Local\\Temp\\ipykernel_46004\\3891957477.py:2: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  df_mean=df.groupby('School')['Height','Weight'].mean()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>School</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fudan University</th>\n",
       "      <td>162.408824</td>\n",
       "      <td>54.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peking University</th>\n",
       "      <td>162.977419</td>\n",
       "      <td>55.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shanghai Jiao Tong University</th>\n",
       "      <td>163.932727</td>\n",
       "      <td>56.442308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tsinghua University</th>\n",
       "      <td>163.149206</td>\n",
       "      <td>54.223881</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Height     Weight\n",
       "School                                              \n",
       "Fudan University               162.408824  54.000000\n",
       "Peking University              162.977419  55.666667\n",
       "Shanghai Jiao Tong University  163.932727  56.442308\n",
       "Tsinghua University            163.149206  54.223881"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 均值\n",
    "df_mean=df.groupby('School')['Height','Weight'].mean()\n",
    "df_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "70094b00",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\86135\\AppData\\Local\\Temp\\ipykernel_46004\\3318859835.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  df_max=df.groupby('School')['Height','Weight'].max()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>School</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fudan University</th>\n",
       "      <td>177.3</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peking University</th>\n",
       "      <td>185.3</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shanghai Jiao Tong University</th>\n",
       "      <td>188.9</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tsinghua University</th>\n",
       "      <td>193.9</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Height  Weight\n",
       "School                                       \n",
       "Fudan University                177.3    81.0\n",
       "Peking University               185.3    87.0\n",
       "Shanghai Jiao Tong University   188.9    89.0\n",
       "Tsinghua University             193.9    79.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 最大值\n",
    "df_max=df.groupby('School')['Height','Weight'].max()\n",
    "df_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d3d2e8cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\86135\\AppData\\Local\\Temp\\ipykernel_46004\\2058141452.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  df_min=df.groupby('School')['Height','Weight'].min()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>School</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fudan University</th>\n",
       "      <td>147.3</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peking University</th>\n",
       "      <td>147.8</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shanghai Jiao Tong University</th>\n",
       "      <td>145.4</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tsinghua University</th>\n",
       "      <td>150.5</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Height  Weight\n",
       "School                                       \n",
       "Fudan University                147.3    34.0\n",
       "Peking University               147.8    34.0\n",
       "Shanghai Jiao Tong University   145.4    34.0\n",
       "Tsinghua University             150.5    36.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 最小值\n",
    "df_min=df.groupby('School')['Height','Weight'].min()\n",
    "df_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "87b3a35b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 男女比例\n",
    "import matplotlib.pyplot as plt \n",
    "series=df.groupby('School')['Gender'].value_counts()\n",
    "\n",
    "series.plot(kind=\"barh\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9c57e112",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "School                         Grade    \n",
       "Fudan University               Junior       12\n",
       "                               Senior       11\n",
       "                               Freshman      9\n",
       "                               Sophomore     8\n",
       "Peking University              Freshman     13\n",
       "                               Junior        8\n",
       "                               Senior        8\n",
       "                               Sophomore     5\n",
       "Shanghai Jiao Tong University  Senior       22\n",
       "                               Junior       17\n",
       "                               Freshman     13\n",
       "                               Sophomore     5\n",
       "Tsinghua University            Junior       22\n",
       "                               Freshman     17\n",
       "                               Sophomore    16\n",
       "                               Senior       14\n",
       "Name: Grade, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grade数量\n",
    "grade=df.groupby('School')['Grade'].value_counts()\n",
    "grade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ca0afa5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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